from collections import OrderedDict
from typing import Tuple, Union

import torch
import torch.nn.functional as F
from torch import nn


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, adapter_type=None, reduction_factor=16, use_bn=True):
        super().__init__()

        # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
        self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)

        self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()

        self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)

        self.relu = nn.ReLU(inplace=True)
        self.downsample = None
        self.stride = stride

        self.adapter_type = adapter_type

        self.front_adapter = None
        self.middle_adapter = None
        self.back_adapter = None
        self.transition_adapter = None

        self.use_bn = use_bn

        if self.adapter_type is not None:
            adapter_pos, adapter_kind = self.adapter_type.split("-")

            # print(adapter_pos, adapter_kind)

            if "front" in adapter_pos:
                self.front_adapter = VisualAdapter(inplanes, planes, adapter_kind, reduction_factor, use_bn)
            
            if "middle" in adapter_pos:
                self.middle_adapter = VisualAdapter(planes, planes, adapter_kind, reduction_factor, use_bn)

            if "back" in adapter_pos:
                self.back_adapter = VisualAdapter(planes, planes * self.expansion, adapter_kind, reduction_factor, use_bn)

            if "transition" in adapter_pos:
                self.transition_adapter = VisualAdapter(planes * self.expansion, planes * self.expansion, adapter_kind, reduction_factor, use_bn)

        if stride > 1 or inplanes != planes * Bottleneck.expansion:
            # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
            self.downsample = nn.Sequential(OrderedDict([
                ("-1", nn.AvgPool2d(stride)),
                ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),
                ("1", nn.BatchNorm2d(planes * self.expansion))
            ]))

    def forward(self, x: torch.Tensor):
        identity = x

        if self.front_adapter is not None:
            adapter_out = self.front_adapter(x)

            if self.use_bn:
                out = self.bn1(self.conv1(x))
                out = self.relu(adapter_out + out)
            else:
                out = self.conv1(x)
                out = self.relu(self.bn1(adapter_out + out))
        else:
            out = self.relu(self.bn1(self.conv1(x)))

        if self.middle_adapter is not None:
            adapter_out = self.middle_adapter(out)

            if self.use_bn:
                out = self.bn2(self.conv2(out))
                out = self.relu(adapter_out + out)
            else:
                out = self.conv2(out)
                out = self.relu(self.bn2(adapter_out + out))
        else:
            out = self.relu(self.bn2(self.conv2(out)))

        out = self.avgpool(out)

        if self.back_adapter is not None:
            adapter_out = self.back_adapter(out)

            if self.use_bn:
                out = self.bn3(self.conv3(out))
                out = adapter_out + out
            else:
                out = self.conv3(out)
                out = self.bn3(adapter_out + out)
        else:
            out = self.bn3(self.conv3(out))

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        if self.transition_adapter is not None:
            adapter_out = self.transition_adapter(out)
            out = self.relu(adapter_out + out)

        return out


class AttentionPool2d(nn.Module):
    def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
        super().__init__()
        self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)
        self.k_proj = nn.Linear(embed_dim, embed_dim)
        self.q_proj = nn.Linear(embed_dim, embed_dim)
        self.v_proj = nn.Linear(embed_dim, embed_dim)
        self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
        self.num_heads = num_heads

    def forward(self, x):
        x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1)  # NCHW -> (HW)NC
        # print(x.shape, self.positional_embedding.shape)
        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (HW+1)NC
        x = x + self.positional_embedding[0, :, None, :].to(x.dtype)  # (HW+1)NC
        x, _ = F.multi_head_attention_forward(
            query=x, key=x, value=x,
            embed_dim_to_check=x.shape[-1],
            num_heads=self.num_heads,
            q_proj_weight=self.q_proj.weight,
            k_proj_weight=self.k_proj.weight,
            v_proj_weight=self.v_proj.weight,
            in_proj_weight=None,
            in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
            bias_k=None,
            bias_v=None,
            add_zero_attn=False,
            dropout_p=0,
            out_proj_weight=self.c_proj.weight,
            out_proj_bias=self.c_proj.bias,
            use_separate_proj_weight=True,
            training=self.training,
            need_weights=False
        )

        return x[0]


# class VisualAdapter(nn.Module):
#     """Conventional Adapter layer, in which the weights of up and down sampler modules
#     are parameters and are optimized."""

#     def __init__(self, input_dim, output_dim, reduction_factor=16):
#         super().__init__()
#         self.down_sample_size = input_dim // reduction_factor
#         self.activation = nn.ReLU(inplace=True)
#         self.down_sampler = nn.Conv2d(input_dim, self.down_sample_size, 1, bias=False)
#         self.up_sampler = nn.Conv2d(self.down_sample_size, output_dim, 1, bias=False)

#         self.norm = nn.BatchNorm2d(output_dim)

#     def forward(self, x):
#         z = self.down_sampler(x)
#         z = self.activation(z)
#         output = self.up_sampler(z)
#         # output = self.norm(output)
#         return output + x


class VisualAdapter(nn.Module):
    """Conventional Adapter layer, in which the weights of up and down sampler modules
    are parameters and are optimized."""

    def __init__(self, input_dim, output_dim, adapter_kind, reduction_factor=16, use_bn=True):
        super().__init__()
        self.adapter_kind = adapter_kind
        self.use_bn = use_bn
        if adapter_kind == "bottleneck":
            self.down_sample_size = input_dim // reduction_factor
            self.activation = nn.ReLU(inplace=True)
            self.down_sampler = nn.Conv2d(input_dim, self.down_sample_size, 1, bias=False)
            self.up_sampler = nn.Conv2d(self.down_sample_size, output_dim, 1, bias=False)

            if use_bn:
                self.bn1 = nn.BatchNorm2d(self.down_sample_size)
                self.bn2 = nn.BatchNorm2d(output_dim)

        elif adapter_kind == "basic":
            self.activation = nn.ReLU(inplace=True)
            self.conv = nn.Conv2d(input_dim, output_dim, 1, bias=False)

            if use_bn:
                self.bn = nn.BatchNorm2d(output_dim)

        else:
            raise NotImplementedError

    def forward(self, x):
        if self.adapter_kind == "bottleneck":
            z = self.down_sampler(x)
            z = self.bn1(z) if self.use_bn else z
            z = self.activation(z)
            output = self.up_sampler(z)
            output = self.bn2(output) if self.use_bn else output

        elif self.adapter_kind == "basic":
            output = self.conv(x)
            output = self.bn(output) if self.use_bn else output

        return output



class ModifiedResNet(nn.Module):
    """
    A ResNet class that is similar to torchvision's but contains the following changes:
    - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
    - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
    - The final pooling layer is a QKV attention instead of an average pool
    """

    def __init__(self, layers, output_dim, heads, input_resolution=224, width=64, adapter_type=None, reduction_factor=1, use_bn=True):
        super().__init__()
        self.output_dim = output_dim
        self.input_resolution = input_resolution

        self.adapter_type = adapter_type
        self.reduction_factor = reduction_factor
        self.use_bn = use_bn

        # the 3-layer stem
        self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(width // 2)
        self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(width // 2)
        self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
        self.bn3 = nn.BatchNorm2d(width)
        self.avgpool = nn.AvgPool2d(2)
        self.relu = nn.ReLU(inplace=True)

        # residual layers
        self._inplanes = width  # this is a *mutable* variable used during construction
        self.layer1 = self._make_layer(width, layers[0])
        self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
        self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
        self.layer4 = self._make_layer(width * 8, layers[3], stride=2)

        embed_dim = width * 32  # the ResNet feature dimension
        self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim)

    def _make_layer(self, planes, blocks, stride=1):
        layers = [Bottleneck(self._inplanes, planes, stride, adapter_type=self.adapter_type, reduction_factor=self.reduction_factor, use_bn=self.use_bn)]

        self._inplanes = planes * Bottleneck.expansion

        # if self.use_adapter:
            # layers.append(VisualAdapter(self._inplanes, self._inplanes, self.reduction_factor))

        for _ in range(1, blocks):
            layers.append(Bottleneck(self._inplanes, planes, adapter_type=self.adapter_type, reduction_factor=self.reduction_factor))

            # if self.use_adapter:
                # layers.append(VisualAdapter(self._inplanes, self._inplanes, self.reduction_factor))

        return nn.Sequential(*layers)

    def forward(self, x):
        def stem(x):
            for conv, bn in [(self.conv1, self.bn1), (self.conv2, self.bn2), (self.conv3, self.bn3)]:
                x = self.relu(bn(conv(x)))
            x = self.avgpool(x)
            return x

        x = x.type(self.conv1.weight.dtype)
        x = stem(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        # print(x.shape)
        # x = self.attnpool(x)
        attnpool = self.attnpool(x)

        return (x, attnpool)


class LayerNorm(nn.LayerNorm):
    """Subclass torch's LayerNorm to handle fp16."""

    def forward(self, x: torch.Tensor):
        orig_type = x.dtype
        ret = super().forward(x.type(torch.float32))
        return ret.type(orig_type)


class QuickGELU(nn.Module):
    def forward(self, x: torch.Tensor):
        return x * torch.sigmoid(1.702 * x)


class ResidualAttentionBlock(nn.Module):
    def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
        super().__init__()

        self.attn = nn.MultiheadAttention(d_model, n_head)
        self.ln_1 = LayerNorm(d_model)
        self.mlp = nn.Sequential(OrderedDict([
            ("c_fc", nn.Linear(d_model, d_model * 4)),
            ("gelu", QuickGELU()),
            ("c_proj", nn.Linear(d_model * 4, d_model))
        ]))
        self.ln_2 = LayerNorm(d_model)
        self.attn_mask = attn_mask

    def attention(self, x: torch.Tensor):
        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
        return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]

    def forward(self, x: torch.Tensor):
        x = x + self.attention(self.ln_1(x))
        x = x + self.mlp(self.ln_2(x))
        return x


class Transformer(nn.Module):
    def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None):
        super().__init__()
        self.width = width
        self.layers = layers
        self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])

    def forward(self, x: torch.Tensor):
        return self.resblocks(x)


class VisualTransformer(nn.Module):
    def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int):
        super().__init__()
        self.input_resolution = input_resolution
        self.output_dim = output_dim
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)

        scale = width ** -0.5
        self.class_embedding = nn.Parameter(scale * torch.randn(width))
        self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))
        self.ln_pre = LayerNorm(width)

        self.transformer = Transformer(width, layers, heads)

        self.ln_post = LayerNorm(width)
        self.proj = nn.Parameter(scale * torch.randn(width, output_dim))

    def forward(self, x: torch.Tensor):
        x = self.conv1(x)  # shape = [*, width, grid, grid]
        x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
        x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
        x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)  # shape = [*, grid ** 2 + 1, width]
        x = x + self.positional_embedding.to(x.dtype)
        x = self.ln_pre(x)

        x = x.permute(1, 0, 2)  # NLD -> LND
        x = self.transformer(x)
        x = x.permute(1, 0, 2)  # LND -> NLD

        # x = self.ln_post(x[:, 0, :])

        x = self.ln_post(x)
        # if self.proj is not None:
        #     x = x @ self.proj

        return x


class CLIP(nn.Module):
    def __init__(self,
                 embed_dim: int,
                 # vision
                 image_resolution: int,
                 vision_layers: Union[Tuple[int, int, int, int], int],
                 vision_width: int,
                 vision_patch_size: int,
                 # text
                 context_length: int,
                 vocab_size: int,
                 transformer_width: int,
                 transformer_heads: int,
                 transformer_layers: int,
                 adapter_type: str,
                 reduction_factor: int,
                 use_bn: bool,
                 ):
        super().__init__()

        self.context_length = context_length

        if isinstance(vision_layers, (tuple, list)):
            vision_heads = vision_width * 32 // 64
            self.visual = ModifiedResNet(
                layers=vision_layers,
                output_dim=embed_dim,
                heads=vision_heads,
                input_resolution=image_resolution,
                width=vision_width,
                adapter_type=adapter_type,
                reduction_factor=reduction_factor,
                use_bn=use_bn,
            )
        else:
            vision_heads = vision_width // 64
            self.visual = VisualTransformer(
                input_resolution=image_resolution,
                patch_size=vision_patch_size,
                width=vision_width,
                layers=vision_layers,
                heads=vision_heads,
                output_dim=embed_dim
            )

        self.transformer = Transformer(
            width=transformer_width,
            layers=transformer_layers,
            heads=transformer_heads,
            attn_mask=self.build_attention_mask()
        )

        self.vocab_size = vocab_size
        self.token_embedding = nn.Embedding(vocab_size, transformer_width)
        self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width))
        self.ln_final = LayerNorm(transformer_width)

        self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim))
        self.logit_scale = nn.Parameter(torch.ones([]))

        self.initialize_parameters()

    def initialize_parameters(self):
        nn.init.normal_(self.token_embedding.weight, std=0.02)
        nn.init.normal_(self.positional_embedding, std=0.01)

        if isinstance(self.visual, ModifiedResNet):
            if self.visual.attnpool is not None:
                std = self.visual.attnpool.c_proj.in_features ** -0.5
                nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std)
                nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std)
                nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std)
                nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std)

            for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]:
                for name, param in resnet_block.named_parameters():
                    if name.endswith("bn3.weight"):
                        nn.init.zeros_(param)

        proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
        attn_std = self.transformer.width ** -0.5
        fc_std = (2 * self.transformer.width) ** -0.5
        for block in self.transformer.resblocks:
            nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
            nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
            nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
            nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)

        if self.text_projection is not None:
            nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)

    def build_attention_mask(self):
        # lazily create causal attention mask, with full attention between the vision tokens
        # pytorch uses additive attention mask; fill with -inf
        mask = torch.empty(self.context_length, self.context_length)
        mask.fill_(float("-inf"))
        mask.triu_(1)  # zero out the lower diagonal
        return mask

    @property
    def dtype(self):
        return self.visual.conv1.weight.dtype

    def encode_image(self, image):
        return self.visual(image.type(self.dtype))

    def encode_text(self, text):
        x = self.token_embedding(text).type(self.dtype)  # [batch_size, n_ctx, d_model]

        x = x + self.positional_embedding.type(self.dtype)
        x = x.permute(1, 0, 2)  # NLD -> LND
        x = self.transformer(x)
        x = x.permute(1, 0, 2)  # LND -> NLD
        x = self.ln_final(x).type(self.dtype)

        # x.shape = [batch_size, n_ctx, transformer.width]
        # take features from the eot embedding (eot_token is the highest number in each sequence)
        x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection

        return x

    def forward(self, image, text):
        image_features = self.encode_image(image)
        text_features = self.encode_text(text)

        # normalized features
        image_features = image_features / image_features.norm(dim=-1, keepdim=True)
        text_features = text_features / text_features.norm(dim=-1, keepdim=True)

        # cosine similarity as logits
        logit_scale = self.logit_scale.exp()
        logits_per_image = logit_scale * image_features @ text_features.t()
        logits_per_text = logit_scale * text_features @ image_features.t()

        # shape = [global_batch_size, global_batch_size]
        return logits_per_image, logits_per_text


def convert_weights(model: nn.Module):
    """Convert applicable model parameters to fp16"""

    def _convert_weights_to_fp16(l):
        if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
            l.weight.data = l.weight.data.half()
            if l.bias is not None:
                l.bias.data = l.bias.data.half()

        if isinstance(l, nn.MultiheadAttention):
            for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]:
                tensor = getattr(l, attr)
                if tensor is not None:
                    tensor.data = tensor.data.half()

        for name in ["text_projection", "proj"]:
            if hasattr(l, name):
                attr = getattr(l, name)
                if attr is not None:
                    attr.data = attr.data.half()

    model.apply(_convert_weights_to_fp16)


def build_model(state_dict: dict, adapter_type=None, reduction_factor=1, use_bn=True):
    vit = "visual.proj" in state_dict

    if vit:
        vision_width = state_dict["visual.conv1.weight"].shape[0]
        vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
        vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
        grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
        image_resolution = vision_patch_size * grid_size
    else:
        counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]]
        vision_layers = tuple(counts)
        vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
        output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5)
        vision_patch_size = None
        assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0]
        image_resolution = output_width * 32

    embed_dim = state_dict["text_projection"].shape[1]
    context_length = state_dict["positional_embedding"].shape[0]
    vocab_size = state_dict["token_embedding.weight"].shape[0]
    transformer_width = state_dict["ln_final.weight"].shape[0]
    transformer_heads = transformer_width // 64
    transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks")))

    model = CLIP(
        embed_dim,
        image_resolution, vision_layers, vision_width, vision_patch_size,
        context_length, vocab_size, transformer_width, transformer_heads, transformer_layers, adapter_type, reduction_factor, use_bn
    )

    for key in ["input_resolution", "context_length", "vocab_size"]:
        if key in state_dict:
            del state_dict[key]

    # if use_adapter:
        
    #     original_keys = list(state_dict.keys())

    #     for key in original_keys:
    #         if key.startswith("visual.layer"):
    #             key_splits = key.split(".")
    #             # update the layer id because of adapter layers
    #             key_splits[2] = str(int(key_splits[2]) * 2)
    
    #             new_key = ".".join(key_splits)

    #             state_dict[new_key] = state_dict.pop(key)

    convert_weights(model)

    try:
        model.load_state_dict(state_dict)
    except RuntimeError as err:
        print("Some keys are mismatched")

        err = str(err).split("\n", 1)[1]
        print(err)

        model.load_state_dict(state_dict, strict=False)

    return model.eval()


if __name__ == "__main__":
    m = ModifiedResNet(layers=[3, 4, 23, 3], output_dim=3000, heads=6, input_resolution=224, width=64, adapter_type="front/back/middle-bottleneck", reduction_factor=26)
    
    # middle-bottleneck: reduction_factor=1: 7.22%, reduction_factor=2: 3.76%
    # middle-basic: 3.74% 
    # front/back-bottleneck: reduction_factor=32: 2.82%, reduction_factor=24: 3.62%
    # front/back-basic: 23.02%
    # transition-bottleneck: reduction_factor=32: 3.82%
    # transition-basic: 38.25%
    # front/back/middle-bottleneck: reduction_factor=26: 3.63%

    def count_parameters(model):
        return sum(p.numel() for p in model.parameters() if p.requires_grad)

    total = count_parameters(m)

    for p in m.parameters():
        p.requires_grad = False

    for name, sub_module in m.named_modules():
        if isinstance(sub_module, (VisualAdapter)):
            print(f"{name} is trainable...")
            # if len(name.split(".")) < 7: # this will not consider layer norms inside adapters then.
            for param_name, param in sub_module.named_parameters():
                param.requires_grad = True

    adapter = count_parameters(m)

    print(f"{adapter} / {total} = {adapter / total * 100:3f}%")